Early detection of systemic and dermatological diseases performs a critical task in developing patient role results and decreasing longstanding healthcare problems. Human nails serve as important indicators of underlying health conditions, as variations in nail color, texture, and morphology are often associated with diseases such as anemia, jaundice, liver disorders, melanoma, and fungal infections. Traditional diagnostic approaches rely heavily on manual visual inspection by medical professionals, which may be subjective and prone to variability. This paper presents a deep learning-based automated framework for primary (or) early disease detection using nail image processing. The proposed system utilizes Convolutional Neural Networks (CNN) to classify nail images into multiple disease categories based primarily on color features. The system integrates image preprocessing, segmentation, feature extraction, and classification within a structured computational pipeline. Experimental validation was conducted using labelled nail image datasets, achieving satisfactory classification performance with an average accuracy of approximately 65% using color-based features, with improved results under optimized training configurations. The study demonstrates that nail color analysis combined with deep learning techniques can provide a non-invasive, low-cost, and scalable screening tool for early disease identification. The proposed framework is suitable for academic research, prototype healthcare systems, and future mobile-based diagnostic applications.